Satellite signal based positioning technologies, which are mainly used outdoors, are usually not suited to deliver a satisfactory performance when used for indoor positioning, since satellite signals of global navigation satellite systems (GNSS), like the global positioning system (GPS) or Beidou or Galileo, do not penetrate through walls and roofs strongly enough for an adequate signal reception indoors. Thus, these positioning technologies are not able to deliver a performance indoors that would enable seamless, equal and accurate positioning outdoors and indoors.
Therefore, several dedicated solutions for indoor positioning have been developed and commercially deployed during the past years. Examples comprise solutions that are based on pseudolites, which are ground based GPS-like short-range beacons, ultra-sound positioning solutions, Bluetooth low energy (BLE) based positioning solutions, cellular network based positioning solutions and wireless local area network (WLAN) based positioning solutions.
A WLAN based positioning solution, for instance, may be divided in two stages, a training stage and a positioning stage. In the training stage, learning data is collected. The data may be collected in the form of fingerprints that are based on measurements by mobile devices. A fingerprint may contain a location estimate and measurements taken from a radio interface. The location estimate may be for example GNSS based, sensor-based, or manually inputted. Results of measurements taken from the radio interface may comprise, by way of example, measured radio signal strengths and an identification of WLAN access points transmitting the radio signals. Collected fingerprint data may be uploaded to a central positioning database in a server or in the cloud, where algorithms may be run to generate radio models of WLAN access points and/or radio maps for positioning purposes. In the positioning stage, the current location of a mobile device may be estimated based on measurements of the mobile device taken from the radio interface and on the data or a subset of data that is available from the training stage.
A similar approach could be used for a positioning that is based on other types of terrestrial transmitters or on a combination of different types of terrestrial transmitters.
A model or radio map based positioning may function either in mobile-based or mobile-assisted mode, the difference being in where the position estimate is calculated. For the mobile-based approach, model data or radio map data that has been generated in the training stage may be transferred to mobile devices by a server as assistance data for use in position determinations. This may be useful for instance for mobile phones, where primarily the mobile device's user is interested in location information. The mobile-assisted mode, in contrast, refers to the case in which the device only makes the appropriate measurements, for e.g. signal strength measurements, and sends the measurement results to another entity, e.g. a server, for position estimation.
Accordingly, these indoor positioning solutions require a central positioning infrastructure for generating the model data or radio map data in the training stage.